1. Field Of The Invention
This invention relates generally to an airline reservation system. In particular, the present invention provides an airline seat inventory control system for computerized airline reservation systems.
2. Description Of Related Art
Strategic and operational planning for commercial airlines are highly complicated problems, especially since the industry has been deregulated. In order to cope with this complexity, computer-based decision support systems have been adopted to facilitate the planning of schedules, routes, aircraft and crew rotations and yield management. Yield (or revenue) management is one of the most important aspects of the operational plan for a commercial airline. Yield management can be separated into two distinct parts: pricing and seat inventory control. Pricing involves the establishment of fare classes and tariffs within those classes for each specific origin-destination market. Seat inventory control is the periodic adjustment of nested booking limits for the various fare classes so as to optimize the passenger mix and thereby maximize the generated revenue. In particular, the objective is to fly the aircraft as full as possible without allowing the earlier-booking, discount-fare passengers to displace the later-booking, full-fare passengers.
Recently, considerable research has been devoted to developing automated seat inventory control methods (For a survey, see the following publications, all of which are incorporated herein by reference: P. P. Belobaba, "Airline yield management, an overview of seat inventory control," Transportation Science, 21, (1987), No. 2, pp. 63-72; for a comparative evaluation see E. L. Williamson, "Comparison of the optimization techniques for origin-destination seat inventory control," Technical Report FTL-R88-2, Flight Transportation Laboratory, Massachusetts Institute of Technology, Cambridge, Mass., May 1988). However, the proposed methods all have serious limitations.
Some methods are leg-based and therefore do not produce booking limits that are optimal in a system-wide sense. For example, the "locally greedy" approach used by these methods may not recognize the additional revenue generated by long-haul (multi-leg-itinerary) passengers versus short-haul (single-leg-itinerary) passengers, or, on the other hand, they may have an uneconomical bias to long-haul passengers. (see, e.g., the following publications, all of which are incorporated herein by reference: K. Littlewood, "Forecasting and control of passenger bookings," Proceedings of the 12th AGIFORS Symposium, 1972, pp. 95-117; A. V. Bhatia and S. C. Parekh, "Optimal allocation of seats by fare," Presentation to the AGIFORS Reservation Study Group, 1973; H. Richter, "The differential revenue method to determine optimal seat allotments by fare type," Proceedings of the 22nd AGIFORS Symposium, 1982, pp. 339-362; P. P. Belobaba, "Air travel demand and airline seat inventory management," Technical Report FTL-R87-8, Flight Transportation Laboratory, Massachusetts Institute of Technology, Cambridge, Mass., May 1987; P. P. Belobaba, "Application of a probabilistic decision model to airline seat inventory control:, Operations Research, 37 (1989), No. 2, pp. 183-197).
Other methods are network-based, but assume a deterministic demand model, i.e., they assume that demand for air travel in a particular market is known precisely without any uncertainty. (see, e.g., the following publication, which is incorporated herein by reference: F. Glover, R. Glover, J. Lorenzo, and C. McMillan, "The passenger-mix problem in the scheduled airlines," Interfaces, 12 (1982), pp. 73-79). Such methods do not reserve enough seats to capture higher-than-average demand for the more expensive fare classes. Further, these methods use linear programming formulations with large numbers of variables (and concomitantly time-consuming solutions) to determine the booking limits for each fare class. Efforts to simultaneously achieve network-wide optimally and account for the probabilistic nature of demand have resulted in 0-1 integer programming formulations with an even larger number of variables (see, e.g., the following publication, which is incorporated herein by reference: R. D. Wollmer, "An airline reservation model for opening and closing fare classes," Unpublished Internal Report, McDonnell-Douglas Corporation, Long Beach, Calif., 1985). The large number of variables and the complexity of the solution methods make these approaches unsuitable for real-world problems.